AB Split Testing Boosts Web Performance Significantly

The Importance Of A/B Split Testing To Keep Improving?

As audiences become more sophisticated, so should your marketing tools, such as using AB split testing. Likewise, if your specific market has previously reacted well to content, products, or particular features, this does not mean that they will respond as well in the future.

Either you keep updating your site’s offerings to attract customers, or your competitors will hijack them by default. In short, if you don’t stay on top of your marketing, you fall behind.

The solution? Keep evolving your marketing efforts to align with growing market demands. One way to respond to your customers is to conduct AB split testing to increase conversions.

What Is A/B Split Testing All About?

When you conduct A/B split tests, you add to your marketing arsenal and stay abreast of what your customers want you to deliver. As you learn more about your customers' shopping behaviors, you’ll meet their growing demands.

But what is A/B split testing?

A/B split testing involves testing an element on your site. This element can be a CTA link, the color of a CTA button, content, or any tool on the site that you use to improve the customer’s experience.

Business owners benefit from the simplicity of the A/B test, as do site customers. This test is convenient to use, is cost-effective, and reaps positive outcomes.

A/B split testing also involves testing similar or the same elements because you cannot viably compare apples to cabbages, for example. Instead, you need to compare similar written content, videos, backgrounds, colors, subject lines, leads, introductions,?Virtual System Load Sharing?(VSLS), and so on.

Also, you can use dual or multivariate split testing tools to increase conversions. However, the simplicity of A/B split testing is appealing as it is typically simpler than using multivariate split testing tools.

The catch is that the simplicity of ab testing often encourages users to get carried away with different variables. As a result, they forget the purpose of the test and the goal they want to achieve. This distraction is one of the pitfalls of AB split testing, which you can easily overcome with clear direction.

Clarify Your Metrics

Before using any AB test, you need to clarify your metrics. Without clear metrics, you cannot effectively track test results, making it a fruitless activity.

So, if you want to increase any performance levels on your website, you should first identify the metrics you want to test to enable effective tracking of the outcomes. Then, once you have the test results, you can apply improvements, which AB testing is all about.

In clarifying your metrics, you can choose to start with any element on your website. For example, you can run several AB tests simultaneously but must always compare apples with apples.

Here are several examples of the tests that you can run using the AB split method:

• If you wish to increase website traffic, you can focus on blog titles. If you aim to enhance the content, you could experiment with changing article headings or your web page's titles. So long as you compare blog titles with blog titles or a web page title with another webpage title, you're conducting AB by using the same metrics.
• Alternatively, you may want to investigate the reason for low bounce rates on your site. If this is the case, you could begin your split test font styles, blog introductions, or the images you use in your posts.
• Likewise, in your new endeavor to update your marketing efforts, you might want to examine the effects of CTAs, anchor texts, or various site locations to test customer conversion rates.

All these elements are well-suited to AB split tests, but remember, you cannot compare conversion rates with bounce rates. Always use the same metrics for authentic results; otherwise, your efforts will be meaningless.

Only Use One Variable Per Test

AB testing is unlike multivariate split testing tools, where you can test multiple variables. Because of this, you can only choose one independent variable and one dependent variable to determine audience responses when you use AB split testing.

Before embarking on an AB test, it is valuable to understand the difference between?independent and dependent variables.

• An independent variable will not change because of other variables
• ?In contrast, a dependent variable does change in relation to other factors, such as the independent variable

Applying the AB test to shopping cart abandonment will look something like this:

You may want to check why customers are not following through with purchases in your shopping cart. Clarifying your metrics for AB may involve changing the position of the shipping cost notification, swapping product images, or the design of the checkout page.

In other words, you can start with the position of the shipping cost notification on the existing checkout page. The shopping cart is the independent variable because its function doesn’t change.

You change the shipping cost notification because you want to test whether its position affects shopping cart abandonment. The position of this notification is, therefore, the dependent variable.

The next step is to design a checkout page with the shipping costs in a different position. If sales go up because of this change, your audience is responding positively. If not, the following step is to test product images or redesign the checkout page after running an AB test on these separate variables.

If you’re still confused about the independent and dependent variables in AB, then use this test:

“The shopping cart (independent variable) prompts a change in the shipping cost notification position (dependent variable). Therefore, it is impossible that the shipping cost notification position (dependent variable) can create a change in the shopping cart (independent variable)”.

Tracking And Measurement Of AB Split Test Results

What must always be clear is that AB involves only two sets of variables—A and B. Another thing that you should be clear about is that these two tests must be run simultaneously to achieve statistically meaningful results.

Statistically meaningful results involve collecting data from at least 1,000 customers for the original shipping cost notification position and the new position. Depending on how busy your website is, this experiment may take a few days, weeks, or months.

Once you have the results, you will understand whether to make the shipping cost notification position permanent. Or you can conduct another AB split test to see what other changes will produce positive results to reduce the shopping cart abandonment issue.

To achieve reliable test results, you need reliable tracking and measuring tools. Unfortunately, a cheap plugin doesn't always provide accurate or dependable results. You can achieve small improvement increments of 4% or more over one AB test period, but our clients confirm reaching improvements up to 80% with repeated testing.

Our AB tests cover every variable on your website where you can make improvements, so the point is that you should (A)lways (B)e split testing to keep your site relevant and profitable.

To learn more about our AB split testing feature and how to use it to improve your sales, click here to book a?demo?or?contact us today.?